




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class lowerCAmelCase__      (	lowercase,      lowercase,      lowercase,      unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__ =     StableDiffusionInpaintPipeline
			lowerCamelCase__ =     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
			lowerCamelCase__ =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
			lowerCamelCase__ =     frozenset(
			    []		)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			lowerCamelCase__ =     frozenset([]		)
			def    A_       (    self    ):
							torch.manual_seed(0    )
							_lowerCamelCase	:     List[Any]       =       UNetaDConditionModel(
							    block_out_channels=(32, 64)    , layers_per_block=2    , sample_size=32    , in_channels=9    , out_channels=4    , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    , cross_attention_dim=32    , attention_head_dim=(2, 4)    , use_linear_projection=lowercase    , )
							_lowerCamelCase	:     Optional[int]       =       PNDMScheduler(skip_prk_steps=lowercase    )
							torch.manual_seed(0    )
							_lowerCamelCase	:     str       =       AutoencoderKL(
							    block_out_channels=[32, 64]    , in_channels=3    , out_channels=3    , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    , latent_channels=4    , sample_size=128    , )
							torch.manual_seed(0    )
							_lowerCamelCase	:     Optional[Any]       =       CLIPTextConfig(
							    bos_token_id=0    , eos_token_id=2    , hidden_size=32    , intermediate_size=37    , layer_norm_eps=1E-05    , num_attention_heads=4    , num_hidden_layers=5    , pad_token_id=1    , vocab_size=1000    , hidden_act='gelu'    , projection_dim=512    , )
							_lowerCamelCase	:     Any       =       CLIPTextModel(lowercase    )
							_lowerCamelCase	:     List[str]       =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

							_lowerCamelCase	:     Optional[int]       =       {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components
			def    A_       (    self    , lowercase    , lowercase=0    ):
							# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
							_lowerCamelCase	:     Any       =       floats_tensor((1, 3, 32, 32)    , rng=random.Random(lowercase    )    ).to(lowercase    )
							_lowerCamelCase	:     Optional[int]       =       image.cpu().permute(0    , 2    , 3    , 1    )[0]
							_lowerCamelCase	:     int       =       Image.fromarray(np.uinta(lowercase    )    ).convert('RGB'    ).resize((64, 64)    )
							_lowerCamelCase	:     Any       =       Image.fromarray(np.uinta(image + 4    )    ).convert('RGB'    ).resize((64, 64)    )
							if str(lowercase    ).startswith('mps'    ):
											_lowerCamelCase	:     Dict       =       torch.manual_seed(lowercase    )
							else:
											_lowerCamelCase	:     Union[str, Any]       =       torch.Generator(device=lowercase    ).manual_seed(lowercase    )
							_lowerCamelCase	:     str       =       {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': init_image,
							    'mask_image': mask_image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							}
							return inputs
			def    A_       (    self    ):
							_lowerCamelCase	:     Optional[Any]       =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_lowerCamelCase	:     Union[str, Any]       =       self.get_dummy_components()
							_lowerCamelCase	:     str       =       StableDiffusionInpaintPipeline(**lowercase    )
							_lowerCamelCase	:     str       =       sd_pipe.to(lowercase    )
							sd_pipe.set_progress_bar_config(disable=lowercase    )

							_lowerCamelCase	:     Dict       =       self.get_dummy_inputs(lowercase    )
							_lowerCamelCase	:     str       =       sd_pipe(**lowercase    ).images
							_lowerCamelCase	:     List[Any]       =       image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							_lowerCamelCase	:     Dict       =       np.array([0.47_27, 0.57_35, 0.39_41, 0.54_46, 0.59_26, 0.43_94, 0.50_62, 0.46_54, 0.44_76]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2






			def    A_       (    self    ):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3    )






@slow
@require_torch_gpu
class lowerCAmelCase__      (	unittest.TestCase		):






			'''simple docstring'''



			def    A_       (    self    ):
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()
			def    A_       (    self    ):
							_lowerCamelCase	:     Any       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_lowerCamelCase	:     Optional[Any]       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )
							_lowerCamelCase	:     Union[str, Any]       =       load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
							    '/yellow_cat_sitting_on_a_park_bench.npy'    )

							_lowerCamelCase	:     str       =       'stabilityai/stable-diffusion-2-inpainting'
							_lowerCamelCase	:     List[str]       =       StableDiffusionInpaintPipeline.from_pretrained(lowercase    , safety_checker=lowercase    )
							pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )
							pipe.enable_attention_slicing()

							_lowerCamelCase	:     Tuple       =       'Face of a yellow cat, high resolution, sitting on a park bench'

							_lowerCamelCase	:     Tuple       =       torch.manual_seed(0    )
							_lowerCamelCase	:     str       =       pipe(
							    prompt=lowercase    , image=lowercase    , mask_image=lowercase    , generator=lowercase    , output_type='np'    , )
							_lowerCamelCase	:     Union[str, Any]       =       output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image    ).max() < 9E-3
			def    A_       (    self    ):
							_lowerCamelCase	:     Tuple       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_lowerCamelCase	:     Tuple       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )
							_lowerCamelCase	:     int       =       load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
							    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'    )

							_lowerCamelCase	:     Tuple       =       'stabilityai/stable-diffusion-2-inpainting'
							_lowerCamelCase	:     int       =       StableDiffusionInpaintPipeline.from_pretrained(
							    lowercase    , torch_dtype=torch.floataa    , safety_checker=lowercase    , )
							pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )
							pipe.enable_attention_slicing()

							_lowerCamelCase	:     Dict       =       'Face of a yellow cat, high resolution, sitting on a park bench'

							_lowerCamelCase	:     Any       =       torch.manual_seed(0    )
							_lowerCamelCase	:     Dict       =       pipe(
							    prompt=lowercase    , image=lowercase    , mask_image=lowercase    , generator=lowercase    , output_type='np'    , )
							_lowerCamelCase	:     List[Any]       =       output.images[0]

							assert image.shape == (512, 512, 3)
							assert np.abs(expected_image - image    ).max() < 5E-1






			def    A_       (    self    ):
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()
							torch.cuda.reset_peak_memory_stats()

							_lowerCamelCase	:     Tuple       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/sd2-inpaint/init_image.png'    )
							_lowerCamelCase	:     int       =       load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'    )

							_lowerCamelCase	:     Tuple       =       'stabilityai/stable-diffusion-2-inpainting'
							_lowerCamelCase	:     List[Any]       =       PNDMScheduler.from_pretrained(lowercase    , subfolder='scheduler'    )
							_lowerCamelCase	:     List[Any]       =       StableDiffusionInpaintPipeline.from_pretrained(
							    lowercase    , safety_checker=lowercase    , scheduler=lowercase    , torch_dtype=torch.floataa    , )
							pipe.to(lowercase    )
							pipe.set_progress_bar_config(disable=lowercase    )
							pipe.enable_attention_slicing(1    )
							pipe.enable_sequential_cpu_offload()

							_lowerCamelCase	:     Union[str, Any]       =       'Face of a yellow cat, high resolution, sitting on a park bench'

							_lowerCamelCase	:     Optional[Any]       =       torch.manual_seed(0    )
							_lowerCamelCase	:     Union[str, Any]       =       pipe(
							    prompt=lowercase    , image=lowercase    , mask_image=lowercase    , generator=lowercase    , num_inference_steps=2    , output_type='np'    , )

							_lowerCamelCase	:     Optional[Any]       =       torch.cuda.max_memory_allocated()
							# make sure that less than 2.65 GB is allocated
							assert mem_bytes < 2.65 * 10**9







"""simple docstring"""


import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
		import onnxruntime as ort




@nightly
@require_onnxruntime
@require_torch_gpu
class 						__UpperCamelCase     (     unittest.TestCase						):
					@property
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Dict:
											return (
											    "CUDAExecutionProvider",
											    {
											        "gpu_mem_limit": "15000000000",  # 15GB
											        "arena_extend_strategy": "kSameAsRequested",
											    },
											)
					@property
					def        UpperCAmelCase__ (							self							:	int   )	->       Optional[Any]:
											lowerCAmelCase   :Any						=			ort.SessionOptions()
											lowerCAmelCase   :Dict						=			False
											return options
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       str:
											lowerCAmelCase   :Union[str, Any]						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo.png'   )
											lowerCAmelCase   :str						=			load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'   )
											lowerCAmelCase   :List[Any]						=			load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'   )

											# using the PNDM scheduler by default
											lowerCAmelCase   :Tuple						=			OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
											    'CompVis/stable-diffusion-v1-4'     , revision='onnx'     , safety_checker=UpperCAmelCase     , feature_extractor=UpperCAmelCase     , provider=self.gpu_provider     , sess_options=self.gpu_options     , )
											pipe.set_progress_bar_config(disable=UpperCAmelCase   )

											lowerCAmelCase   :Optional[Any]						=			'A red cat sitting on a park bench'

											lowerCAmelCase   :Optional[Any]						=			np.random.RandomState(0   )
											lowerCAmelCase   :Union[str, Any]						=			pipe(
											    prompt=UpperCAmelCase     , image=UpperCAmelCase     , mask_image=UpperCAmelCase     , strength=0.7_5     , guidance_scale=7.5     , num_inference_steps=15     , generator=UpperCAmelCase     , output_type='np'     , )

											lowerCAmelCase   :Optional[int]						=			output.images[0]

											assert image.shape == (512, 512, 3)
											assert np.abs(expected_image - image   ).max() < 1e-2